Diagnostic for colorectal cancer

ABSTRACT

The present invention provides a method for diagnosing or detecting colorectal cancer in a subject, the method comprising determining the presence and/or level of biomarkers selected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1β, TGFβ1, and TIMP-1. The invention also relates to diagnostic kits comprising reagents for determining the presence and/or level of the biomarkers and methods of detecting or diagnosing colorectal cancer.

FIELD OF THE INVENTION

The present invention relates to determining the presence and/or levelof biomarkers for detecting or diagnosing colorectal cancer. Theinvention also relates to diagnostic kits comprising reagents fordetermining the presence and/or level of the biomarkers and methods ofdetecting or diagnosing colorectal cancer.

BACKGROUND OF THE INVENTION

Colorectal cancer, also referred to as colon cancer or bowel cancer, isthe second most common cause of cancer worldwide. There is an annualincidence of almost a million colorectal cancer cases with an annualmortality around 500,000 (Cancer in Australia: an overview, 2008).Unfortunately, 30-50% of patients have occult or overt metastases atpresentation and once tumours have metastasized prognosis is very poorwith a five year survival of less than 10% (Etzioni et al., 2003). Bycontrast, greater than 90% of patients who present while the tumour isstill localised will still be alive after 5 years and can be consideredcured. The early detection of colorectal lesions would thereforesignificantly reduce the impact of colon cancer (Etzioni et al., 2003).

The current screening assays in widespread use for the diagnosis ofcolorectal cancer are the faecal occult blood test (FOBT), flexiblesigmoidoscopy, and colonoscopy (Lieberman, 2010). FOBT has relativelylow specificity resulting in a high rate of false positives. Allpositive FOBT must therefore be followed up with colonoscopy. Samplingis done by individuals at home and requires at least two consecutivefaecal samples to be analysed to achieve optimal sensitivity. Someversions of the FOBT also require dietary restrictions prior tosampling. FOBT also lacks sensitivity for early stage cancerous lesionsthat do not bleed into the bowel and as stated above, these are thelesions for which treatment is most successful.

While FOBT screening does result in reduction of mortality due tocolorectal cancer it suffers from a low compliance rate (30-40%), mostlikely due to the unpalatable nature of the test, which limits itsusefulness as a screening tool. Colonoscopy is the current gold standardand has a specificity of greater then 90% but it is intrusive and costlywith a small but finite risk of complications (2.1 per 1000 procedures)(Levin, 2004). Development of a rapid, specific, cheap blood based assaywould overcome compliance issues commonly seen with other screeningtests (Tonus, 2006; Hundt et al., 2007) and would be more acceptable aspart of a large screening assay.

SUMMARY OF THE INVENTION

The present inventors investigated over sixty biomarkers associated withcolorectal cancer, but found that none of the biomarkers alone would besuitable as a diagnostic test. Surprisingly, it was found thatdetermining the presence and/or level of at least two biomarkersassociated with colorectal cancer in a sample from a subject allowed forthe detection or diagnosis of colorectal cancer at any of the stages ofdisease. Determining the presence and/or level of at least twobiomarkers advantageously provides a diagnostic test that is at leastcomparable in sensitivity and specificity to the FOBT.

Accordingly, in one aspect, the present invention provides a method fordiagnosing or detecting colorectal cancer in a subject, the methodcomprising:

i) determining the presence and/or level of at least two biomarkersselected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1β,TGFβ1, and TIMP-1 in a sample from the subject,

wherein the presence and/or level of the two biomarkers is indicative ofcolorectal cancer.

In one embodiment, the method comprises determining the presence and/orlevel of two biomarkers selected from M2PK, EpCam, IL-13, DKK-3, IL-8and IGFBP2.

In another embodiment, the method comprises determining the presenceand/or level of expression of at least three of the biomarkers.

In one embodiment, the three biomarkers are selected from M2PK, EpCam,IL-13, DKK-3, IL-8, IGFBP2, MIP1β, TGFβ1 and MAC2BP.

In one particular embodiment, the method comprises determining thepresence and/or level of three biomarkers, wherein the three biomarkersare:

i) DKK-3, M2PK, and IGFBP2;

ii) M2PK, IGFBP2, and EpCAM;

iii) M2PK, MIP1β, and TGFβ1; or

iv) IL-8, IL-13, and MAC2BP.

In another embodiment, the method comprises determining the presenceand/or level of expression of at least four of the biomarkers.

In one particular embodiment, the method comprises determining thepresence and/or level of four biomarkers, wherein the four biomarkersare:

i) DKK-3, M2PK, MAC2BP, and IGFBP2;

ii) IL-8, IL-13, MAC2BP, and EpCam;

iii) DKK3, M2PK, TGFβ1, and TIMP-1;

iv) M2PK, MIP1β, IL-13, and TIMP-1; or

v) IL-8, MAC2BP, IGFBP2, and EpCam.

In yet another embodiment, the method comprises determining the presenceand/or level of at least five of the biomarkers.

In one particular embodiment, the five biomarkers are IL-8, IGFBP2,MAC2BP, M2PK, and IL-13.

In another embodiment, the method comprises determining the presenceand/or level of at least six of the biomarkers.

In another embodiment, the method comprises determining the presenceand/or level of at least seven of the biomarkers.

In one particular embodiment, the seven biomarkers are:

i) IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, and TGF β1; or

ii) IL-8, IGFBP2, MAC2BP, M2PK, IL-13, EpCam, and MIP1β.

In yet another embodiment, the method comprises determining the presenceand/or level of at least eight of the biomarkers.

In one embodiment, the method comprises determining the presence and/orlevel of at least nine of the biomarkers.

In yet another embodiment, the method comprises determining the presenceand/or level of at least ten of the biomarkers.

In another embodiment, the method comprises determining the presenceand/or level of a combination of biomarkers as provided in any of Tables7 to 18.

In another embodiment, the method comprises detecting the presenceand/or level of least one additional biomarker selected from IGF-I,IGF-II, IGF-BP2, Amphiregulin, VEGFA, VEGFD, MMP-1, MMP-2, MMP-3, MMP-7,MMP-9, TIMP-1, TIMP-2, ENA-78, MCP-1, MIP-1β, IFN-γ, IL-10, IL-13,IL-1β, IL-4, IL-8, IL-6, MAC2BP, Tumor M2 pyruvate kinase, M65, OPN,DKK-3, EpCam, TGFβ-1, and VEGFpan.

In one embodiment, the method diagnoses or detects colorectal cancerwith a sensitivity of at least 50%.

In another embodiment, the method diagnoses or detects colorectal cancerwith a sensitivity of at least 66%.

In yet another embodiment, the method diagnoses or detects colorectalcancer with a sensitivity of at least 77%.

In one embodiment, the method diagnoses or detects colorectal cancerwith a specificity of at least 75%.

In one embodiment, the method diagnoses or detects colorectal cancerwith a specificity of at least 80%.

In another embodiment, the method diagnoses or detects colorectal cancerwith a specificity of at least 90%.

In yet another embodiment, the method diagnoses or detects colorectalcancer with a specificity of at least 95%.

In another embodiment, the method diagnoses or detects Dukes Stage Acolorectal cancer with a sensitivity of at least 50% and a specificityof at least 95%.

In yet another embodiment, the method diagnoses or detects Dukes Stage Acolorectal cancer with a sensitivity of at least 60% and a specificityof at least 80%.

In another embodiment, the method diagnoses or detects Dukes Stage Acolorectal cancer with a sensitivity of at least 50% and a specificityof at least 90%.

The skilled person will understand that Dukes Stage A corresponds to TNMClassifications T1, N0, M0 and T2, N0, M0.

Thus in one embodiment, the method diagnoses or detects TNMClassification T1, N0, M0 or T2, N0, M0 colorectal cancer with asensitivity of at least 50% and a specificity of at least 95%.

In yet another embodiment, the method diagnoses or detects TNMClassification T1, N0, M0 or T2, N0, M0 colorectal cancer with asensitivity of at least 60% and a specificity of at least 80%.

In another embodiment, the method diagnoses or detects TNMClassification T1, N0, M0 or T2, N0, M0 colorectal cancer with asensitivity of at least 50% and a specificity of at least 90%.

Any suitable technique for the detection of polypeptides may be used inthe methods of the invention. In one embodiment, the method comprisescontacting the sample with at least one compound that binds a biomarkerpolypeptide. Alternatively, the method comprises detecting thepolypeptides by mass spectrometry.

In one particular embodiment, the compound is detectably labelled.

In another embodiment, the compound is an antibody.

In one embodiment, the compound is bound to a solid support.

In the methods of the invention, determining the presence and/or levelof the biomarker may comprise determining the presence and/or level of apolynucleotide encoding the biomarker, such as a biomarker genetranscript. Thus, in one embodiment, the biomarkers are polynucleotides.

In yet another embodiment of the methods of the invention, the methodcomprises:

i) determining the presence and/or level of the biomarkers in the samplefrom the subject; and

ii) comparing the presence and/or level of the biomarkers to a control,wherein a presence and/or level in the sample that is different to thecontrol is indicative of colorectal cancer.

In one embodiment, the sample comprises blood, plasma, serum, urine,platelets, magakaryocytes or faeces.

In another aspect, the present invention provides a method of treatmentcomprising:

(i) diagnosing or detecting colorectal cancer according to the method ofthe invention; and

(ii) administering or recommending a therapeutic for the treatment ofcolorectal cancer.

In yet another aspect, the present invention provides a method formonitoring the efficacy of treatment of colorectal cancer in a subject,the method comprising treating the subject for colorectal cancer andthen detecting the presence and/or level of at least two biomarkersselected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1β,TGFβ1, and TIMP-1 in a sample from the subject, wherein an absence ofand/or reduction in the level of expression of the polypeptides aftertreatment when compared to before treatment is indicative of effectivetreatment.

In another aspect, the present invention provides an array of at leasttwo compounds for the diagnosis or detection of colorectal cancer,wherein each of the compounds binds a different biomarker polypeptideselected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1β,TGFβ1, and TIMP-1.

In yet another aspect, the present invention provides a kit fordiagnosing or detecting colorectal cancer in a subject, the kitcomprising two compounds that each binds a different biomarkerpolypeptide selected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3,EpCam, MIP1β, TGFβ1, and TIMP-1.

Throughout this specification the word “comprise”, or variations such as“comprises” or “comprising”, will be understood to imply the inclusionof a stated element, integer or step, or group of elements, integers orsteps, but not the exclusion of any other element, integer or step, orgroup of elements, integers or steps.

As will be apparent, preferred features and characteristics of oneaspect of the invention are applicable to many other aspects of theinvention.

The invention is hereinafter described by way of the followingnon-limiting Examples and with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

FIG. 1. In Study 3 an optimum combination of the 46 potential proteinbiomarkers was found using logistic regression modelling, resulting in apanel of seven biomarkers and is illustrated as a ROC curve (blackcurve). The performance of this “panel” on independent data wasestimated using “leave one out” cross-validation (grey curve). Thevertical lines are drawn at points of 80% and 90% specificity—operatingpoints of interest in screening tests. Performance statistics are givenin Table 5.

FIG. 2. Performance of a seven biomarker model identifying colorectalcancer patients from normals at each Dukes Stage illustrated by ROCcurves for each stage. A (red)—Stage A, B (green)—Stage B, C(blue)—Stage C, and D (black)-Stage D from Study 3a. Performancecharacteristics are given in Table 6.

FIG. 3. When biomarker results from Study 4 (also referred to as Study 3remeasured) were modelled in pairs a total of 5 pairs (out of a possible45 combinations selected from the list of 10 biomarkers above) could beshown to produce a sensitivity above 52% at a specificity of 95. Theperformance of these pair wise biomarker combinations is illustrated asROC curves (n=5 curves). Performance characteristics are given in Table7.

FIG. 4. An example of a 3 biomarker model generated from Study 4 datawhich had a sensitivity of at least 50% at 95% specificity. There were968 possible 3-biomarker combinations and approximately half of thosecombinations showed a performance of at least 50% sensitivity at 90%specificity.

FIG. 5. ROC curves are illustrated for all combinations of 3-10biomarkers generated from Study 4 data which have a sensitivity of atleast 50% at 95% specificity (n=485 cross validated curves out of apossible 968 models).

FIG. 6. Frequency of each biomarker in the best 485 models. These BMsrepresent all serum models that gave a sensitivity of at least 50% at95% The high representation of all 10 biomarkers in the useful modelsdemonstrates the unity of our selection of these 10 biomarkers.

FIG. 7. A 5 biomarker model generated from Study 4 data is illustratedas a ROC curve (black) and cross validated ROC curve (grey). This modelshows a sensitivity of 68% at 95% specificity when all stages of diseaseare included and when cross validated gave a sensitivity of 64%.Biomarkers included are [IL-8, IGFBP2, Mac2BP, DKK-3 and M2PK].

FIG. 8. A 6 biomarker model generated from Study 4 data is illustratedas a ROC curve (black) and cross validated ROC curve (Grey). This modelshows a sensitivity of 77% at a specificity of 95% when all stages ofdisease are included and when cross validated gave a sensitivity of 67%.Biomarkers included are [IL-8, IGFBP2, Mac2BP, DKK-3, TGFbeta1&M2PK].

FIG. 9. Two alternative seven biomarker models generated from Study 3adata are shown. One was optimised for high specificity (black/new) andan alternative or model optimised for area under the curve is shown(grey/old). At 90% specificity the sensitivity was 72% for the new modeland 77% for the older model. Biomarkers included were as follows:

New: IL8, IGFBP2, s90MAC2BP, M2PK, DKK-3, IL-13 & TGFbeta,

Old: IL8, IGFBP2, s90MAC2BP, M2PK, EpCAM, IL13 & MIP-1b.

FIG. 10. A seven biomarker model generated from Study 4 data isillustrated as a ROC curve (black) and cross validated ROC curve (grey).This model shows, a sensitivity of 84% at a specificity of 95%.Biomarkers included are [M2PK serum, IL8.plasma, TGF beta1.serum,IGFBP2.plasma, Mac2BP.serum, TIMP1.plasma and Dkk3 plasma.

FIG. 11. Cross validated ROC curves showing the performance of a 3biomarker model for each Dukes stage is illustrated. This datademonstrates the validity of the choice of three biomarkers (DKK-3, M2PKand IGFBP2) for detecting cancer at different stages of the diseaseprogression. The data indicates that at Stage A if the three markers areused, the test still will achieve a significant sensitivity of 64% at95% specificity which is comparable to the sensitivity achieved at latestage disease (79%). That is the biomarker panel of three will pick upearly disease states allowing early detection. Biomarkers included areDkk3, M2PK and IGFBP2.

KEY TO THE SEQUENCE LISTING

-   SEQ ID NO:1—amino acid sequence of IL-8-   SEQ ID NO:2—amino acid sequence of IGFBP2-   SEQ ID NO:3—amino acid sequence of MAC2BP-   SEQ ID NO:4—amino acid sequence of M2PK variant 1-   SEQ ID NO:5—amino acid sequence of M2PK variant 2-   SEQ ID NO:6—amino acid sequence of M2PK variant 3-   SEQ ID NO:7—amino acid sequence of IL-13-   SEQ ID NO:8—amino acid sequence of DKK-3 variant 1-   SEQ ID NO:9—amino acid sequence of DKK-3 variant 2-   SEQ ID NO:10—amino acid sequence of DKK-3 variant 3-   SEQ ID NO:11—amino acid sequence of EpCam-   SEQ ID NO:12—amino acid sequence of MIP1β-   SEQ ID NO:13—amino acid sequence of TGFβ1-   SEQ ID NO:14—amino acid sequence of TIMP-1

DETAILED DESCRIPTION General Techniques and Definitions

Unless specifically defined otherwise, all technical and scientificterms used herein shall be taken to have the same meaning as commonlyunderstood by one of ordinary skill in the art (e.g., in cell culture,molecular genetics, immunology, immunohistochemistry, protein chemistry,and biochemistry).

Unless otherwise indicated, the recombinant protein, cell culture, andimmunological techniques utilized in the present invention are standardprocedures, well known to those skilled in the art. Such techniques aredescribed and explained throughout the literature in sources such as, J.Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons(1984), J. Sambrook et al., Molecular Cloning: A Laboratory Manual,3^(rd) edn, Cold Spring Harbour Laboratory Press (2001), R. Scopes,Protein Purification—Principals and Practice, 3^(rd) edn, Springer(1994), T. A. Brown (editor), Essential Molecular Biology: A PracticalApproach, Volumes 1 and 2, IRL Press (1991), D. M. Glover and B. D.Hames (editors), DNA Cloning: A Practical Approach, Volumes 1-4, IRLPress (1995 and 1996), and F. M. Ausubel et al. (editors), CurrentProtocols in Molecular Biology, Greene Pub. Associates andWiley-Interscience (1988, including all updates until present), EdHarlow and David Lane (editors) Antibodies: A Laboratory Manual, ColdSpring Harbour Laboratory, (1988), and J. E. Coligan et al. (editors)Current Protocols in Immunology, John Wiley & Sons (including allupdates until present).

As used herein, the term “colorectal cancer”, also known as “coloncancer”, “bowel cancer” or “rectal cancer”, refers to all forms ofcancer originating from the epithelial cells lining the large intestineand/or rectum.

As used herein, “biomarker” refers to any molecule, such as a gene, genetranscript (for example mRNA), peptide or protein or fragment thereofproduced by a subject which is useful in differentiating subjects havingcolorectal cancer from normal or healthy subjects.

As used herein, the term “diagnosis”, and variants thereof such as, butnot limited to, “diagnose”, “diagnosed” or “diagnosing” shall not belimited to a primary diagnosis of a clinical state, but should be takento include diagnosis of recurrent disease.

As used herein, the term “subject” refers to any animal that may developcolorectal cancer and includes animals such as mammals, e.g. humans, ornon-human mammals such as cats and dogs, laboratory animals such asmice, rats, rabbits or guinea pigs, and livestock animals. In apreferred embodiment, the subject is a human.

The “sample” may be of any suitable type and may refer, e.g., to amaterial in which the presence or level of biomarkers can be detected.Preferably, the sample is obtained from the subject so that thedetection of the presence and/or level of biomarkers may be performed invitro. Alternatively, the presence and/or level of biomarkers can bedetected in vivo. The sample can be used as obtained directly from thesource or following at least one step of (partial) purification. Thesample can be prepared in any convenient medium which does not interferewith the method of the invention. Typically, the sample is an aqueoussolution, biological fluid, cells or tissue. Preferably, the sample isblood, plasma, serum, urine, platelets, megakaryocytes or faeces.Pre-treatment may involve, for example, preparing plasma from blood,diluting viscous fluids, and the like. Methods of treatment can involvefiltration, distillation, separation, concentration, inactivation ofinterfering components, and the addition of reagents. The selection andpre-treatment of biological samples prior to testing is well known inthe art and need not be described further.

As used herein the terms “treating”, “treat” or “treatment” includeadministering a therapeutically effective amount of a compoundsufficient to reduce or delay the onset or progression of colorectalcancer, or to reduce or eliminate at least one symptom of colorectalcancer.

Biomarkers

The present inventors have shown that determining the presence and/orlevel of least two biomarkers in a sample from a subject allows for thedetection or diagnosis of colorectal cancer, either early detection atDukes Stage A or at some later stage such as Dukes Stage B or C or D,with specificity and sensitivity comparable to or greater than thatachieved with the FOBT. The at least two biomarkers that are useful inthe methods of the present invention are selected from IL-8(interleukin-8), IGFBP2 (insulin-like growth factor binding protein-2),MAC2BP (MAC2-binding protein; serum protein 90K), M2PK (pyruvate kinasemuscle 2, pyruvate kinase 3), IL-13 (interleukin-13), DKK-3 (dickkopfhomolog, 3), EpCAM (epithelial cell adhesion molecule), MIP1β(macrophage inflammatory protein 1β, CCL4, MIP1beta), TGFβ1(transforming growth factor β1 , TGFbeta1) and TIMP-1 (tissue inhibitorof metalloproteinase 1). Reference to any of these biomarkers includesreference to all polypeptide and polynucleotide variants such asisoforms and transcript variants as would be known by the person skilledin the art. NCBI accession numbers of representative sequences for eachof the biomarkers are provided in Table 1.

TABLE 1 NCBI accession numbers for representative biomarker sequences.Biomarker Representative NCBI Accession Numbers IL-8 NM_000584.2 (SEQ IDNO: 1) IGFBP2 NM_000597.2 (SEQ ID NO: 2) MAC2BP NM_005567.3 (SEQ ID NO:3) M2PK NM_002654.3; NM_182470.1; NM_182471.1 (SEQ ID NOs: 4-6) IL-13NM_002188.2 (SEQ ID NO: 7) DKK-3 NM_015881.5; NM_013253; NM_001018057.1(SEQ ID NOs: 8-10) EpCam NM_002354.2 (SEQ ID NO: 11) MIP1β NM_002984.2(SEQ ID NO: 12) TGFβ1 NM_000660.4 (SEQ ID NO: 13) TIMP-1 NM_003254.2(SEQ ID NO: 14)

Detecting or Diagnosing Colorectal Cancer

It will be apparent from the preceding description that the diagnosticmethods of the present invention may involve a degree of quantificationto determine levels biomarkers in patient samples. Such quantificationis readily provided by the inclusion of appropriate control samples.

In one embodiment, internal controls are included in the methods of thepresent invention. A preferred internal control is one or more samplestaken from one or more healthy individuals.

In the present context, the term “healthy individual” shall be taken tomean an individual who is known not to suffer from colorectal cancer,such knowledge being derived from clinical data on the individual,including, but not limited to, a different diagnostic assay to thatdescribed herein.

As will be known to those skilled in the art, when internal controls arenot included in each assay conducted, the control may be derived from anestablished data set.

Data pertaining to the control subjects are preferably selected from thegroup consisting of:

1. a data set comprising measurements of the presence or level ofexpression of biomarkers for a typical population of subjects known tohave colorectal cancer;

2. a data set comprising measurements of the presence or level ofbiomarkers for the subject being tested wherein said measurements havebeen made previously, such as, for example, when the subject was knownto be healthy or, in the case of a subject having colorectal cancer,when the subject was diagnosed or at an earlier stage in diseaseprogression;

3. a data set comprising measurements of the presence or level ofbiomarkers for a healthy individual or a population of healthyindividuals; and

4. a data set comprising measurements of the presence or level ofbiomarkers for a normal individual or a population of normalindividuals.

In the present context, the term “typical population” with respect tosubjects known to have colorectal cancer shall be taken to refer to apopulation or sample of subjects diagnosed with colorectal cancer thatis representative of the spectrum of colorectal cancer patients. This isnot to be taken as requiring a strict normal distribution ofmorphological or clinicopathological parameters in the population, sincesome variation in such a distribution is permissible. Preferably, a“typical population” will exhibit a spectrum of colorectal cancer atdifferent stages of disease progression. It is particularly preferredthat a “typical population” exhibits the expression characteristics of acohort of subjects as described herein.

The term “normal individual” shall be taken to mean an individual thatdoes not express a biomarker, or expresses a biomarker at a low level ina sample. As will be known to those skilled in the art, data obtainedfrom a sufficiently large sample of the population will normalize,allowing the generation of a data set for determining the average levelof a particular biomarker.

Those skilled in the art are readily capable of determining the baselinefor comparison in any diagnostic assay of the present invention withoutundue experimentation, based upon the teaching provided herein.

Compounds that bind a biomarker when used diagnostically may be linkedto a diagnostic reagent such as a detectable label to allow easydetection of binding events in vitro or in vivo. Suitable labels includeradioisotopes, dye markers or other imaging reagents for detectionand/or localisation of target molecules. Compounds linked to adetectable label can be used with suitable in vivo imaging technologiessuch as, for example, radiology, fluoroscopy, nuclear magnetic resonanceimaging (MRI), CAT-scanning, positron emission tomography (PET),computerized tomography etc.

The diagnostic methods of the present invention are able to diagnose ordetect colorectal cancer with a sensitivity and specificity that is atleast comparable to FOBT, or greater. As would be understood by theperson skilled in the art, sensitivity refers to the proportion ofactual positives in the diagnostic test which are correctly identifiedas having colorectal cancer. Specificity measures the proportion ofnegatives which are correctly identified as not having colorectalcancer. In one embodiment, the methods of the invention are able todiagnose or detect colorectal cancer with a sensitivity of at least 50%,60% or 66%, or at least 77%, 80%, 83%, 85%, 86%, 87%, 88%, 89%, 90%, orat least 93%. In another embodiment, the methods of the invention areable to diagnose or detect colorectal cancer with a sensitivity of atleast 80%, or at least 85% or at least 90%, or at least 95%.

In one embodiment, the methods of the invention are able to diagnose ordetect colorectal cancer with a specificity of at least 75%, 80%, 85%,90%, 91%, 92%, 93%, 94% or at least 95%.

Advantageously, the methods of the present invention are able to detectcolorectal cancer at all of the Dukes Stages with greater sensitivitythan the FOBT. In Dukes Stage A, the tumor has penetrated into, but notthrough, the bowel wall. In Dukes Stage B, the tumor has penetratedthrough the bowel wall but there is not yet any lymph node involvement.In Dukes Stage C, the cancer involves regional lymph nodes. In DukesStage D, there is distant metastasis, for example, to the liver or lung.In one embodiment, the methods of the present invention are able todiagnose or detect colorectal cancer at any Dukes Stage with asensitivity of at least 80%.

As known to the skilled person, there are other systems for stagingcancer that are know in the art. One example is the TMN Classificationof Malignant Tumors (TNM) that is used by the American Joint Committeeon Cancer (AJCC: Colon and rectum. in Edge et al., eds; AJCC CancerStaging Manual, 7^(th) ed. New York, N.Y.: Springer, 2010, pp: 143-164).Another example is the Modified Astler-Coller classification (MAC).

Accordingly, the skilled person will appreciate that the Dukes Stagescorrespond to certain TNM Classifications. For example, Dukes Stage Acorresponds to T1, T2, N0 and M0; Dukes Stage B corresponds to T3, T4a,T4b, N0 and M0; and Dukes Stage C corresponds to i) T1-T2, N1/N1c, M0;ii) T1, N2a and M0; iii) T3-T4a, N1/N1c and M0; iv) T2-T3, N2a and M0;v) T1-T2, N2b and M0; vi) T4a, N2a and M0; vii) T3-T4a, N2b and M0; andviii) T4b, N1-N2 and M0. Thus, the skilled person will understand thatreference to a Dukes Stage as used herein includes reference to thecorresponding TMN classification as known in the art.

Protein Detection Techniques

In one embodiment, biomarker polypeptide is detected in a patientsample, wherein the presence and/or level of the polypeptide in thesample is indicative of colorectal cancer. For example, the method maycomprise contacting a biological sample derived from the subject with acompound capable of binding to a biomarker polypeptide, and detectingthe formation of complex between the compound and the biomarkerpolypeptide. The term “biomarker polypeptide” as used herein includesfragments of biomarker polypeptides, including for example, immunogenicfragments and epitopes of the biomarker polypeptide.

In one embodiment, the compound that binds the biomarker is an antibody.

The term “antibody” as used herein includes intact molecules as well asmolecules comprising or consisting of fragments thereof, such as, forexample Fab, F(ab′)2, Fv and scFv, as well as engineered variantsincluding diabodies, triabodies, mini-bodies and single-domainantibodies which are capable of binding an epitopic determinant. Thus,antibodies may exist as intact immunoglobulins, or as modifications in avariety of forms.

In another embodiment, an antibody to a biomarker polypeptide isdetected in a patient sample, wherein the presence and/or level of theantibody in the sample is indicative of colorectal cancer.

Preferred detection systems contemplated herein include any known assayfor detecting proteins or antibodies in a biological sample isolatedfrom a human subject, such as, for example, SDS/PAGE, isoelectricfocussing, 2-dimensional gel electrophoresis comprising SDS/PAGE andisoelectric focussing, an immunoassay, flow cytometry e.g.fluorescence-activated cell sorting (FACS), a detection based systemusing an antibody or non-antibody compound, such as, for example, asmall molecule (e.g. a chemical compound, agonist, antagonist,allosteric modulator, competitive inhibitor, or non-competitiveinhibitor, of the protein). In accordance with these embodiments, theantibody or small molecule may be used in any standard solid phase orsolution phase assay format amenable to the detection of proteins.Optical or fluorescent detection, such as, for example, using massspectrometry, MALDI-TOF, biosensor technology, evanescent fiber optics,or fluorescence resonance energy transfer, is clearly encompassed by thepresent invention. Assay systems suitable for use in high throughputscreening of mass samples, e.g. a high throughput spectroscopy resonancemethod (e.g. MALDI-TOF, electrospray MS or nano-electrospray MS), arealso contemplated. Another suitable protein detection technique involvesthe use of Multiple Reaction Monitoring (MRM) in LC-MS (LC/MRM-MS)(Anderson and Hunter, 2006).

Immunoassay formats are particularly suitable, e.g., selected from thegroup consisting of, an immunoblot, a Western blot, a dot blot, anenzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA),enzyme immunoassay. Modified immunoassays utilizing fluorescenceresonance energy transfer (FRET), isotope-coded affinity tags (ICAT),matrix-assisted laser desorption/ionization time of flight (MALDI-TOF),electrospray ionization (ESI), biosensor technology, evanescentfiber-optics technology or protein chip technology are also useful.

Nucleic Acid Detection Techniques

Any suitable technique that allows for the qualitative and/orquantitative assessment of the level of a biomarker polynucleotide in asample may be used. The terms “nucleic acid molecule” or“polynucleotide” as used herein refer to an oligonucleotide,polynucleotide or any fragment thereof.

Comparison may be made by reference to a standard control, or to acontrol level that is found in healthy tissue. For example, levels of atranscribed gene can be determined by Northern blotting, and/or RT-PCR.With the advent of quantitative (real-time) PCR, quantitative analysisof gene expression can be achieved by using appropriate primers for thegene of interest. The nucleic acid may be labelled and hybridised on agene array, in which case the gene concentration will be directlyproportional to the intensity of the radioactive or fluorescent signalgenerated in the array.

Methods for direct sequencing of nucleotide sequences are well known tothose skilled in the art and can be found for example in Ausubel et al.,eds., Short Protocols in Molecular Biology, 3rd ed., Wiley, (1995) andSambrook et al., Molecular Cloning, 3rd ed., Cold Spring HarborLaboratory Press, (2001). Sequencing can be carried out by any suitablemethod, for example, dideoxy sequencing, chemical sequencing orvariations thereof. Direct sequencing has the advantage of determiningvariation in any base pair of a particular sequence.

Other PCR methods that may be used in carrying out the invention includehybridization based PCR detection systems, TaqMan assay (U.S. Pat. No.5,962,233) and the molecular beacon assay (U.S. Pat. No. 5,925,517).

The nucleic acid may be separated from the sample for testing. Suitablemethods will be known to those of skill in the art. For example, RNA maybe isolated from a sample to be analysed using conventional procedures,such as are supplied by QIAGEN technology. This RNA is thenreverse-transcribed into DNA using reverse transcriptase and the DNAmolecule of interest may then be amplified by PCR techniques usingspecific primers.

Diagnostic procedures may also be performed directly upon patientsamples. Hybridisation or amplification assays, such as, for example,Southern or Northern blot analysis, immunohistochemistry,single-stranded conformational polymorphism analysis (SSCP) and PCRanalyses are among techniques that are useful in this respect. Ifdesired, target or probe nucleic acid may be immobilised to a solidsupport such as a microtitre plate, membrane, polystyrene bead, glassslide or other solid phase.

Kits

The present invention provides kits for the diagnosis or detection ofcolorectal cancer. Such kits may be suitable for detection of nucleicacid species, or alternatively may be for detection of a polypeptidegene product, as discussed above.

For detection of polypeptides, antibodies will most typically be used ascomponents of kits. However, any agent capable of binding specificallyto a biomarker gene product will be useful in this aspect of theinvention. Other components of the kits will typically include labels,secondary antibodies, substrates (if the gene is an enzyme), inhibitors,co-factors and control gene product preparations to allow the user toquantitate expression levels and/or to assess whether the diagnosisexperiment has worked correctly. Enzyme-linked immunosorbent assay-based(ELISA) tests and competitive ELISA tests are particularly suitableassays that can be carried out easily by the skilled person using kitcomponents.

Optionally, the kit further comprises means for the detection of thebinding of an antibody to a biomarker polypeptide. Such means include areporter molecule such as, for example, an enzyme (such as horseradishperoxidase or alkaline phosphatase), a dye, a radionucleotide, aluminescent group, a fluorescent group, biotin or a colloidal particle,such as colloidal gold or selenium. Preferably such a reporter moleculeis directly linked to the antibody.

In yet another embodiment, a kit may additionally comprise a referencesample. In one embodiment, a reference sample comprises a polypeptidethat is detected by an antibody. Preferably, the polypeptide is of knownconcentration. Such a polypeptide is of particular use as a standard.Accordingly, various known concentrations of such a polypeptide may bedetected using a diagnostic assay described herein.

For detection of nucleic acids, such kits may contain a first containersuch as a vial or plastic tube or a microtiter plate that contains anoligonucleotide probe. The kits may optionally contain a secondcontainer that holds primers. The probe may be hybridisable to DNA whosealtered expression is associated with colorectal cancer and the primersare useful for amplifying this DNA. Kits that contain an oligonucleotideprobe immobilised on a solid support could also be developed, forexample, using arrays (see supplement of issue 21(1) Nature Genetics,1999).

For PCR amplification of nucleic acid, nucleic acid primers may beincluded in the kit that are complementary to at least a portion of abiomarker gene as described herein. The set of primers typicallyincludes at least two oligonucleotides, preferably fouroligonucleotides, that are capable of specific amplification of DNA.Fluorescent-labelled oligonucleotides that will allow quantitative PCRdetermination may be included (e.g. TaqMan chemistry, MolecularBeacons). Suitable enzymes for amplification of the DNA, will also beincluded.

Control nucleic acid may be included for purposes of comparison orvalidation. Such controls could either be RNA/DNA isolated from healthytissue, or from healthy individuals, or housekeeping genes such asβ-actin or GAPDH whose mRNA levels are not affected by colorectalcancer.

Regression Algorithms and Statistics

In order to develop a panel of biomarkers suitable for diagnosing ordetecting colorectal cancer, the present inventors have analysednumerous biomarkers in a statistical model. Such an improvement in theperformance of a test is sometimes referred to as the “in-sample”performance. A fair evaluation of a test requires its assessment usingout-of-sample subjects, that is, subjects not included in theconstruction of the initial predictive model. This is achieved byassessing the test performance using cross validation.

Tests for statistical significance include linear and non linearregression, including ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney andodds ratio, Baysian probability algorithms. As the number of biomarkersmeasured increases however, it can be generally more convenient to use amore sophisticated technique such as Random Forests, simple logistic,Bayes Net to name a few.

For example, Bayesian probability may be adopted. In this circumstance a10-fold cross-validation can be used to estimate the “out-of-sample”performance of the models in question. For each combination ofbiomarkers under consideration, the data can be divided randomly into 10sub-samples, each with similar proportions of healthy subject andsubjects at each stage of disease. In turn, each subsample can beexcluded, and a logistic model built using the remaining 90% of thesubjects. This model can then be used to estimate the probability ofcancer for the excluded sub-sample, providing an estimate of“out-of-sample” performance. By repeating this for the remaining 9subsamples, “out-of-sample” performance can be estimated from the studydata itself. These out-of sample predicted probabilities can then becompared with the actual disease status of the subjects to create aReceiver Operating Characteristic (ROC) Curve, from which thecross-validated sensitivity at 95% specificity may be estimated.

Each estimate of “out-of-sample” performance using cross-validation (orany other method), whilst unbiased, has an element of variability to it.Hence a ranking of models (based on biomarker combinations) can beindicative only of the relative performance of such models. However aset of biomarkers which is capable of being used in a large number ofcombinations to generate a diagnostic test as demonstrated via“out-of-sample” performance evaluations, almost certainly containswithin itself combinations of biomarkers that will withstand repeatedevaluation.

Many different combinations can qualify as diagnostic tests which proveuseful and cost effective and have acceptable sensitivity for a givenspecificity. As an example, consider the five biomarkers: IL-8, IGFBP2,MAC2BP, M2PK and DKK-3. A model discriminating subjects with cancer fromhealthy controls can be as follows:

${\log \left( \frac{p}{1 - p} \right)} = {\beta_{0} + {\beta_{{IL}\; 8}C_{{IL}\; 8}} + {\beta_{{IGFBP}\; 2}C_{{IGFBP}\; 2}} + {\beta_{{MAC}\; 2\; {BP}}C_{{MAC}\; 2\; {BP}}} + {\beta_{M\; 2\; {PK}}C_{M\; 2{PK}}} + {\beta_{{DKK}\; 3}C_{{DKK}\; 3}}}$

Here p represents the probability that a person has colorectal cancer.Each C_(i) is the logarithm of concentration biomarker i in the plasma(or serum) of a person. Each beta (β) is a coefficient applying to thatbiomarker in the concentration units in which it is measured—β₀ is an“offset” or “intercept”. This linear logistic model is common to allresults presented herein, but is far from the only way in which acombination of biomarker concentrations may be modelled to predict theprobability of cancer.

Other non linear or linear logistic algorithms that would be equallyapplicable include Random Forest, ANOVA, t-Test, Fisher analysis,Support Vector Machine, Linear Models for MicroArray data (LIMMA) and/orSignificance Analyses of Microarray Data (SAM), Best First, GreedyStepwise, Naive Bayes, Linear Forward Selection, Scatter Search, LinearDiscriminant Analysis (LDA), Stepwise Logistic Regression, ReceiverOperating Characteristic and Classification Trees (CT).

Thus, in light of the teachings of the present specification, the personskilled in the art will appreciate that the sensitivity and specificityof a test for diagnosing colorectal cancer may be modulated by selectinga different combination of the biomarkers as described herein

Knowledge-Based Systems

It will be apparent from the discussion herein that knowledge-basedcomputer software and hardware for implementing an algorithm also formpart of the present invention. Such computer software and/or hardwareare useful for performing a method of diagnosing or detecting colorectalcancer according the invention. Thus, the present invention alsoprovides software or hardware programmed to implement an algorithm thatprocesses data obtained by performing the method of the invention via amultivariate analysis to provide a disease score and provide or permit adiagnosis or detection of colorectal cancer and/or determine progressionor status of a colorectal cancer or determine whether or not acolorectal cancer has progressed or determine whether or not a subjectis responding to treatment for colorectal cancer in accordance with theresults of the disease score in comparison with predetermined values.

In one example, a method of the invention may be used in existingknowledge-based architecture or platforms associated with pathologyservices. For example, results from a method described herein aretransmitted via a communications network (e.g. the internet) to aprocessing system in which an algorithm is stored and used to generate apredicted posterior probability value which translates to the score ofdisease probability or risk of recurrence or metastasis orresponsiveness to treatment which is then forwarded to an end user inthe form of a diagnostic or predictive report.

The method of the invention may, therefore, be in the form of a kit orcomputer-based system which comprises the reagents necessary to detectthe concentration of the biomarkers and the computer hardware and/orsoftware to facilitate determination and transmission of reports to aclinician.

The assay of the present invention permits integration into existing ornewly developed pathology architecture or platform systems. For example,the present invention contemplates a method of allowing a user todetermine the status of a subject with respect to colorectal cancer, themethod including:

(a) receiving data in the form of levels at least two biomarkersselected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1β,TGFβ1, and TIMP-1 in a readily obtained sample, optionally incombination with another marker of colorectal cancer;(b) processing the subject data via multivariate analysis (for example,regression analysis) to provide a disease score;(c) determining the status of the subject in accordance with the resultsof the disease score in comparison with predetermined values; and(d) transferring an indication of the status of the subject to the uservia the communications network reference to the multivariate analysisincludes an algorithm which performs the multivariate analysis function.

In one embodiment, the method for diagnosing or detecting colorectalcancer of the invention may be performed by taking a blood sample from apatient and determining the presence and/or level of any one or more ofthe biomarkers as described herein. If desired, the measurements may bemade, for example, on a biochip so that a single analysis can be used tomeasure the presence and/or level of multiple biomarkers. The results ofthis analysis may then be inputted into into a computer program thatsubjects them to linear regression analysis. The computer could alsocontain information as to control values or expected ranges, or theclinician, nurse, medical administrator or general practitioner couldinput such data. This analysis wold then provide a score or likelihoodof having colorectal cancer. If a second test for the patient isperformed, the regression analysis may indicate a change in the score,thus indicating that the patient's disease has progressed or worsened.

EXAMPLES Materials and Methods Patient Samples

A collection of plasma and serum samples was taken and processed from acohort of colorectal cancer patients (Dukes Stages A-D) that were beingtreated at several hospitals.

Blood was also collected and processed from a group of about 50 healthyvolunteers over the age of 65 and from a group of 15 over the age of 50.

Four separate studies were undertaken with slightly differentbiomarkers. Study 1 looked at 52 cancer samples and 50 controls, study 2looked at 55 cancer samples and 53 controls, study 3 and 4 looked at 96cancer samples and 50 controls. In study 2, 3 and 4 the patients wereage and gender matched across Dukes Stages, see Table 2 for summarystatistics.

TABLE 2 Characteristics of normal volunteers and colorectal cancerpatients used in studies 2, 3 and 4. Controls Cancers n = 50 n = 96Gender male 25 48 female 25 48 Mean Age (yr) 68 68 Dukes stage A 22 B 30C 30 D 14 Tumour site colon 73 rectum 17 unknown 6 Proximal (includescaecum, ascending, hepatic 43 flexure and transverse colon) Distal(includes splenic flexure, descending, 47 sigmoid and rectum)

Biomarker Analysis

Analysis of biomarkers was done with commercial kits and sourcedantibodies (DSL, R&D Duoset, Calbiotech, Millipore, Abnova, Genway,Peviva, Schebo, Bender) and using ELISA or Luminex assays.

Statistical Evaluation and Panel Biomarker Modelling

Results for each assay were analysed using the statistical softwarepackages Prism and “R”. Individual performance of markers was evaluatedusing the non-parametric Mann-Whitney t-test and individual receiveroperator characteristic (ROC) curves were generated.

Logistic regression and related modelling strategies were used to findcombinations of biomarkers that best separated controls and colorectalcancer patients. Four separate studies were performed with the samesamples/aliquots. The results of each of these is given below.

Results of Study 1, 2 and 3

Biomarkers chosen to be measured in Study 1 and 2 and 3 are listed inTable 3. Biomarkers in bold were those identified as promising from eachstudy (i.e. they were significantly different in samples from colorectalcancer patients versus control and/or they were identified in panels ofcombined biomarkers that distinguish colorectal cancer from controls).

TABLE 3 Biomarkers analysed in studies. Study 1 Study 2 Study 3 IGF-IIGF-BP2 IGF-BP2 (DSL) IGF-II IGF-II IGF-II IGF-BP2 IGF-BP3 IFNg IGF-BP3Her2 TNFa BTC VegFA IL-10 Amphiregulin VegFC IL-6 VegFA VegFD GM-CSFVegFC TIMP-1 IL-12 VegFD TIMP-2 IL-13 MMP-2 MMP-1 IL-8 MMP-7 MMP-2 IL-4MMP-9 MMP-3 Il-2 TIMP-2 MMP-7 IL-1b Her2 MMP-8 MMP-1 MMP-12 MMP-2 MMP-13MMP-3 ENA-78 MMP-7 MCP-1 MMP-8 MIP-1beta MMP-9 GM-CSF ENA-78 IFN-gammaMIP-1alpha IL-10 MIP-1beta IL-12 MCP-1 IL-13 Mac-2BP IL-1beta TIMP-1IL-2 TIMP-2 IL-4 Gro-alpha IL-6 Tumour M2 pyruvate kinase IL-8M30-apoptosense TNF-alpha M65 Cripto Trail-R2 P-cadherin OPN Dkk-3 EpCamTGFbeta1 REG IV CEA DcR3 CA19.9 Amphiregulin CEACAM6 VegFA pan VegFA165bSpondin-2 survivin

Statistical Evaluation and Panel Biomarker Modelling

To find combinations of biomarkers that best separated controls andcolorectal cancer patients, forward variable selection with BayesianInformation Criteria to penalize log-likelihood to prevent over-fittingwas adopted. To estimate the likely performance of the panel ofbiomarkers on an independent dataset, “N-Fold” or “leave-one-out” crossvalidation was used. In this procedure one observation at a time wasexcluded whilst the entire model fitting algorithm was applied to theremaining observations.

The resulting model was then used to estimate the probability that theexcluded observation is a case. This was repeated for each observationin the dataset. In this way each observation in turn acted as anindependent test of the model-building algorithm. The resulting datasetconsisting of cases and controls each with an “independently predicted”case probability can then be compared with the original model. Theability to choose from numerous biomarkers and weight them appropriatelyallows a search strategy which optimises performance in regions ofinterest on the ROC curve. The cost of poor specificity is large numbersof unnecessary colonoscopies.

In study 3, 48 potential biomarkers were evaluated to select a candidatepanel of colorectal cancer biomarkers, using block randomization withinplates to avoid bias. From this list of 48 only 42 showed measurablelevels. Individually 14 biomarkers showed significant difference betweencontrols and CRC as assessed by t-tests; (IGFII, IGFBP2, IL-8, IL-6,MMP-1, MMP-7, s90/Mac2BP, M2PK, EpCam, TIMP-1 (serum and plasma), M65,OPN, TGFβ1, VEGFpan. As expected, none had sufficient sensitivity orspecificity to be useful as a biomarker by itself (not shown). However,using a variety of modelling strategies, including use of logarithmicvalues, several different panels of biomarkers were found that exceededthe performance of FOBT especially for early to late stage disease.

FIG. 1 shows the results from a 7 biomarker panel which included IL8(serum), IL-13 (serum), EpCAM (plasma), M2PK (plasma), IGFBP2 (serum)and Mac2BP (serum) and which was cross validated to predict itsperformance on independent samples.

This 7 biomarker model, which is described at least conceptually as

${\log \left( \frac{p}{1 - p} \right)} = {\beta_{0} + {\beta_{{IL}\; 8}C_{{IL}\; 8}} + {\beta_{{IGFBP}\; 2}C_{{IGFBP}\; 2}} + {\beta_{{MAC}\; 2\; {BP}}C_{{MAC}\; 2\; {BP}}} + {\beta_{M\; 2\; {PK}}C_{M\; 2{PK}}} + {\beta_{{DKK}\; 3}C_{{DKK}\; 3}}}$

provided good performance at high specificity and was robust under crossvalidation. The coefficients estimated to give the best model for thisbiomarker combination in plasma are listed in Table 4. Performancestatistics are provided in Table 5. This performance exceeds that quotedfor FOBT (sensitivity 65.8%, specificity 95%) (Morikawa et al., 2005).

TABLE 4 Coefficients for the biomarker combination. MeasuredConcentration Biomarker in Units Coefficient Intercept NA NA −37.74 IL-8serum pg/mL 1.07 IL-13 serum pg/mL −0.28 EpCAM plasma pg/mL −0.33 M2PKplasma units/mL 1.40 IGFBP2 serum ng/mL 1.99 Mac2BP serum ng/mL 2.39MIP1beta Serum Pg/ml −1.19

TABLE 5 Performance of the 7 biomarker model and cross-validation. Modelestimate Cross validation Area Under the ROC Curve (AUC) 0.91 0.86Sensitivity at 80% specificity 0.84 0.78 Sensitivity at 90% specificity0.81 0.69

This model was also applied separately to patients from each stage ofcolorectal cancer (Dukes Stage A, B, C, D) and shown to perform equallywell within each stage (FIG. 2). The AUCs were 0.88-0.93 and were almostequally good at discriminating all Stages of colorectal cancer. Themodel shows the highest sensitivity of 90% at 90% specificity for StageC and the lowest sensitivity of 73% at 90% specificity for Stage B(Table 6).

TABLE 6 Performance of the Model by Dukes Stage Stage A Stage B Stage CStage D Area Under the ROC Curve 0.89 0.88 0.93 0.91 (AUC) Sensitivityat 80% specificity 0.82 0.77 0.90 0.93 Sensitivity at 90% specificity0.77 0.73 0.90 0.86

Study 4 (Also Referred to as “Study 3 Remeasured”)

In study 4, 10 biomarkers were remeasured in the same cohort as Study 3.Blood was collected from 96 colorectal cancer patients and 50 normalsubjects (the controls). In this study the focus was on 10 biomarkers,namely IGFBP2, IL8, IL13, Mac2BP, M2PK, Dkk3, EpCam, TGFbeta1, TIMP-1,MIP1beta. Assays were performed as described previously. Both serum andplasma levels of each of the biomarkers were measured and compared withcontrol values.

When modelled in pairs (two markers), a total of 5 pairs (out of apossible 45 combinations selected from the list of 10 biomarkers above)could be shown to produce a sensitivity above 52% at a specificity of95%. See Table 7 and FIG. 3.

TABLE 7 Biomarker Pairs Producing Useful Screening Tests onCross-Validation. Estimated Estimated In-Sample Out-of-Sample (Test)(Cross-Validated) Sensitivity at 95% Sensitivity at 95% Biomarker 1Biomarker 2 Specificity Specificity M2PK EpCAM 58.3% 58.3% M2PK IL1356.3% 57.3% Dkk3 M2PK 55.2% 55.2% M2PK IL8 60.4% 54.2% M2PK IGFBP2 58.3%52.1%

In analysing combinations of three to ten of the nominated biomarkers,there are 968 possible combinations. The 968 combinations of between 3and 10 biomarkers consist of the 120 combinations of 3 marker; 210combinations of 4 markers; 252 combinations of 5 markers; 210combinations of 6 markers; 120 combinations of 7 markers; 45combinations of 8 markers; 10 combinations of 9 markers and the singlecombination that includes all 10 biomarkers. When they were modelledusing a linear logistic model, and then tested via 10-fold crossvalidation, about half of the 968 combinations had a sensitivity of 50%at a specificity of 95%, see FIG. 4 which shows the results for a threebiomarker combination. More than half of these combinations would have aspecificity of 90% and a sensitivity of 50%.

FIG. 5 shows all 485 of the estimated out-of sample (10-foldcross-validated) ROC curves for tests out of a total possible 968 modelsbased on all possible combinations of 3 to 10 of the biomarkers. Notethat many individual segments of the 485 ROC curves are coincident, dueto as each horizontal segment represents one control and each verticalsegment one case. In this instance 50.1% of the combinations haveexceeded the 50% sensitivity, 95% specificity, The best estimated“out-of-sample” performance is a sensitivity of 76% at 95% specificity.Repeating the cross-validation will select a different set of models—thesensitivity of any one combination may vary by 10% at 95% specificitydue to random sampling—but result in a similar proportion of useful“useful screening tests”. Precise validation of individual modelsrequires repeated experiments and larger sample sizes.

FIG. 6 shows how many of the 485 combinations with 50% sensitivity, 95%specificity, include any given biomarker. At the high end, 432 of thechosen “useful” combinations include M2PK. At the low end 227 of thechosen “useful” combinations include MIP1beta. This high representationof all 10 biomarkers in “useful” models shows the unity andself-complementarity of the selection of these 10 biomarkers.

FIG. 7 to FIG. 11 demonstrate some of the results from this last study(Study 4) for combinations of 5 and 7 biomarkers, including a modelwhere the samples are either from plasma or serum cluster. FIG. 11demonstrates the validity of the choice of three biomarkers (DKK-3, M2PKand IGFBP2) at different stages of the disease progression. The dataindicates that at Stage A if the three markers are used, the test stillwill achieve a significant sensitivity (64%) at 95% specificity which iscomparable to the sensitivity achieved at late stage disease, 79%). Thatis the biomarker panel of three will pick up early disease statesallowing early detection.

Tables 8 to 16 list results from various combinations of variousbiomarker panel sets. Depending on the linear regression that is used,as well as the cohort control and other factors such as samplederivation and assay kit technique, there may be a variation on theactual figures or order of the markers. Regardless, many of thesecombinations will achieve good selectivity at high specificities so asto be useful for diagnosing or detecting colorectal cancer at any stageof the disease progression.

10

TABLE 8 Combination of three biomarkers in serum that equal or exceed50% sensitivity at 95% specificity. Cross Test Validated SensitivitySensitivity at 95% at 95% BM1 BM2 BM3 Specificity Specificity Dkk3 M2PKIGFBP2 72.9% 70.8% Dkk3 M2PK IL8 62.5% 61.5% M2PK IL13 IGFBP2 65.6%61.5% M2PK IGFBP2 EpCAM 63.5% 61.5% M2PK IL8 IGFBP2 65.6% 60.4% M2PK IL8IL13 61.5% 58.3% M2PK MIP1beta IL13 55.2% 57.3% M2PK IL8 Mac2BP 59.4%57.3% M2PK MIP1beta TGFbeta1 57.3% 56.3% M2PK IL8 EpCAM 64.6% 56.3% Dkk3M2PK IL13 59.4% 55.2% Dkk3 M2PK EpCAM 56.3% 55.2% M2PK MIP1beta EpCAM59.4% 55.2% M2PK IL8 TGFbeta1 58.3% 55.2% M2PK IL8 TIMP1 57.3% 55.2%TGFbeta1 Mac2BP TIMP1 54.2% 55.2% M2PK Mac2BP IGFBP2 58.3% 54.2% Dkk3IL8 Mac2BP 55.2% 53.1% M2PK MIP1beta Mac2BP 52.1% 53.1% M2PK MIP1betaIGFBP2 57.3% 53.1% M2PK TIMP1 EpCAM 58.3% 53.1% M2PK MIP1beta IL8 56.3%52.1% M2PK IL13 TIMP1 58.3% 52.1% Dkk3 M2PK Mac2BP 57.3% 51.0% Dkk3 M2PKTIMP1 55.2% 51.0% M2PK IL13 Mac2BP 58.3% 51.0% M2PK TGFbeta1 IGFBP252.1% 51.0% M2PK TGFbeta1 TIMP1 57.3% 51.0% M2PK TGFbeta1 Mac2BP 56.3%50.0% IL8 IL13 Mac2BP 61.5% 50.0% IL8 TGFbeta1 Mac2BP 53.1% 50.0% M2PKMIP1beta TIMP1 49.0% 49.0% M2PK TGFbeta1 EpCAM 49.0% 47.9% IL8 IL13IGFBP2 49.0% 47.9% IL8 Mac2BP IGFBP2 57.3% 47.9% Dkk3 M2PK MIP1beta55.2% 46.9% TGFbeta1 Mac2BP IGFBP2 46.9% 46.9% Dkk3 Mac2BP IGFBP2 49.0%45.8% M2PK IL13 EpCAM 50.0% 45.8% IL8 Mac2BP TIMP1 52.1% 45.8% IL13Mac2BP IGFBP2 45.8% 44.8% Dkk3 IL8 TGFbeta1 41.7% 43.8% MIP1beta IL8Mac2BP 42.7% 43.8% MIP1beta IL8 EpCAM 44.8% 43.8% IL8 TGFbeta1 EpCAM46.9% 43.8% Dkk3 IL8 EpCAM 51.0% 42.7% IL8 IGFBP2 EpCAM 43.8% 42.7% M2PKMac2BP TIMP1 51.0% 41.7%

TABLE 9 Combination of four biomarkers including DKK-3 in serum thatequal or exceed 50% sensitivity at 95% specificity. Cross Test ValidatedSensitivity Sensitivity at 95% at 95% BM1 BM2 BM3 BM4 SpecificitySpecificity Dkk3 M2PK Mac2BP IGFBP2 68.8% 69.8% Dkk3 M2PK IL8 IL13 71.9%68.8% Dkk3 M2PK IL8 EpCAM 70.8% 67.7% Dkk3 M2PK TGFbeta1 Mac2BP 67.7%65.6% Dkk3 M2PK IL8 IGFBP2 69.8% 64.6% Dkk3 M2PK IL8 Mac2BP 69.8% 63.5%Dkk3 M2PK MIP1beta TGFbeta1 65.6% 61.5% Dkk3 M2PK IL8 TIMP1 68.8% 61.5%Dkk3 M2PK IL13 IGFBP2 63.5% 61.5% Dkk3 M2PK MIP1beta IL8 59.4% 60.4%Dkk3 M2PK IGFBP2 EpCAM 68.8% 60.4% Dkk3 M2PK MIP1beta IGFBP2 69.8% 59.4%Dkk3 M2PK TGFbeta1 IGFBP2 61.5% 59.4% Dkk3 M2PK IL13 Mac2BP 56.3% 58.3%Dkk3 M2PK TGFbeta1 TIMP1 65.6% 58.3% Dkk3 M2PK MIP1beta IL13 57.3% 57.3%Dkk3 IL8 Mac2BP IGFBP2 62.5% 56.3% Dkk3 M2PK IL8 TGFbeta1 65.6% 55.2%Dkk3 M2PK IL13 TIMP1 57.3% 55.2% Dkk3 M2PK Mac2BP TIMP1 57.3% 55.2% Dkk3M2PK MIP1beta Mac2BP 57.3% 54.2% Dkk3 IL8 IL13 Mac2BP 61.5% 54.2% Dkk3M2PK TGFbeta1 EpCAM 61.5% 53.1% Dkk3 M2PK IGFBP2 TIMP1 61.5% 53.1% Dkk3M2PK IL13 TGFbeta1 63.5% 52.1% Dkk3 M2PK TIMP1 EpCAM 56.3% 52.1% Dkk3IL8 Mac2BP TIMP1 57.3% 52.1% Dkk3 M2PK MIP1beta EpCAM 60.4% 51.0% Dkk3TGFbeta1 Mac2BP TIMP1 59.4% 51.0%

TABLE 10 Combination of four biomarkers including M2PK in serum thatequal or exceed 50% sensitivity at 95% specificity. Cross Test ValidatedSensitivity Sensitivity at 95% at 95% BM1 BM2 BM3 BM4 SpecificitySpecificity M2PK IL8 Mac2BP TIMP1 63.5% 65.6% M2PK Mac2BP IGFBP2 EpCAM70.8% 65.6% M2PK IL8 IL13 Mac2BP 66.7% 64.6% M2PK IL8 TGFbeta1 Mac2BP65.6% 64.6% M2PK MIP1beta IL13 IGFBP2 64.6% 62.5% M2PK IL8 IL13 TIMP164.6% 62.5% M2PK IL8 IL13 EpCAM 65.6% 62.5% M2PK IL13 Mac2BP IGFBP269.8% 62.5% M2PK MIP1beta IL8 IL13 63.5% 61.5% M2PK IL8 Mac2BP EpCAM65.6% 61.5% M2PK IL13 IGFBP2 EpCAM 69.8% 61.5% M2PK MIP1beta IL8TGFbeta1 58.3% 58.3% M2PK IL8 IL13 IGFBP2 67.7% 58.3% M2PK IL8 Mac2BPIGFBP2 61.5% 58.3% M2PK IL8 IGFBP2 EpCAM 64.6% 58.3% M2PK IL13 TGFbeta1IGFBP2 64.6% 58.3% M2PK IL13 TGFbeta1 EpCAM 62.5% 58.3% M2PK IL13 IGFBP2TIMP1 62.5% 58.3% M2PK TGFbeta1 Mac2BP TIMP1 62.5% 58.3% M2PK TGFbeta1IGFBP2 EpCAM 61.5% 58.3% M2PK MIP1beta IL13 TIMP1 57.3% 57.3% M2PKMIP1beta TGFbeta1 TIMP1 58.3% 57.3% M2PK MIP1beta IGFBP2 EpCAM 65.6%57.3% M2PK MIP1beta IL8 Mac2BP 54.2% 56.3% M2PK IL8 IL13 TGFbeta1 64.6%56.3% M2PK IL8 TIMP1 EpCAM 62.5% 56.3% M2PK IL13 Mac2BP TIMP1 57.3%56.3% M2PK TGFbeta1 Mac2BP IGFBP2 60.4% 56.3% M2PK IGFBP2 TIMP1 EpCAM63.5% 56.3% M2PK MIP1beta IL8 TIMP1 57.3% 55.2% M2PK IL8 TGFbeta1 TIMP157.3% 55.2% M2PK MIP1beta IL13 Mac2BP 57.3% 54.2% M2PK MIP1beta IL8EpCAM 62.5% 53.1% M2PK MIP1beta TIMP1 EpCAM 59.4% 52.1% M2PK IL13TGFbeta1 TIMP1 64.6% 52.1% M2PK IL13 Mac2BP EpCAM 51.0% 52.1% M2PKMIP1beta IL13 TGFbeta1 57.3% 51.0% M2PK MIP1beta Mac2BP IGFBP2 57.3%51.0% M2PK TGFbeta1 Mac2BP EpCAM 52.1% 51.0% M2PK TGFbeta1 TIMP1 EpCAM59.4% 51.0% M2PK MIP1beta IL8 IGFBP2 52.1% 50.0% M2PK Mac2BP TIMP1 EpCAM52.1% 50.0% M2PK MIP1beta TGFbeta1 Mac2BP 63.5% 49.0% M2PK MIP1beta IL13EpCAM 50.0% 47.9% M2PK MIP1beta TGFbeta1 EpCAM 53.1% 47.9% M2PK IL13TGFbeta1 Mac2BP 60.4% 46.9% M2PK MIP1beta TGFbeta1 IGFBP2 49.0% 44.8%

TABLE 11 Combination of five biomarkers in serum that equal or exceed50% sensitivity at 95% specificity. Test Cross Validated Sensitivity atSensitivity at BM1 BM2 BM3 BM4 BM5 95% Specificity 95% Specificity Dkk3M2PK IL8 IL13 Mac2BP 74.0% 70.8% Dkk3 M2PK IL8 IL13 TIMP1 71.9% 70.8%M2PK TGFbeta1 Mac2BP IGFBP2 EpCAM 69.8% 70.8% Dkk3 M2PK MIP1beta IL8IL13 71.9% 69.8% Dkk3 M2PK MIP1beta IGFBP2 EpCAM 71.9% 69.8% Dkk3 M2PKIL8 IGFBP2 EpCAM 78.1% 69.8% Dkk3 M2PK TGFbeta1 Mac2BP IGFBP2 68.8%69.8% M2PK MIP1beta Mac2BP IGFBP2 EpCAM 69.8% 68.8% Dkk3 M2PK IL8TGFbeta1 Mac2BP 70.8% 67.7% Dkk3 M2PK Mac2BP IGFBP2 EpCAM 70.8% 67.7%M2PK MIP1beta IL8 IL13 Mac2BP 66.7% 67.7% Dkk3 M2PK IL8 IL13 IGFBP270.8% 66.7% Dkk3 M2PK IL13 TGFbeta1 IGFBP2 68.8% 66.7% Dkk3 M2PK IL13IGFBP2 EpCAM 66.7% 66.7% M2PK IL13 Mac2BP IGFBP2 EpCAM 71.9% 66.7% Dkk3M2PK IL8 IL13 TGFbeta1 69.8% 65.6% Dkk3 M2PK IL8 Mac2BP TIMP1 67.7%65.6% Dkk3 M2PK TGFbeta1 Mac2BP EpCAM 66.7% 65.6% M2PK IL8 IL13 TGFbeta1Mac2BP 71.9% 65.6% M2PK IL8 IL13 TGFbeta1 IGFBP2 66.7% 65.6% M2PK IL8TGFbeta1 Mac2BP IGFBP2 64.6% 65.6% M2PK IL8 TGFbeta1 Mac2BP EpCAM 69.8%65.6% Dkk3 M2PK MIP1beta IL8 IGFBP2 66.7% 64.6% Dkk3 M2PK MIP1betaMac2BP IGFBP2 70.8% 64.6% M2PK MIP1beta IL8 IL13 TIMP1 65.6% 64.6% M2PKMIP1beta IL8 TGFbeta1 Mac2BP 65.6% 64.6% M2PK IL8 IL13 Mac2BP EpCAM76.0% 64.6% M2PK IL8 IL13 IGFBP2 EpCAM 72.9% 64.6% M2PK IL8 TGFbeta1Mac2BP TIMP1 64.6% 64.6% M2PK IL8 Mac2BP IGFBP2 EpCAM 71.9% 64.6% Dkk3M2PK IL13 TGFbeta1 TIMP1 68.8% 63.5% Dkk3 M2PK IGFBP2 TIMP1 EpCAM 67.7%63.5% M2PK MIP1beta IL13 IGFBP2 EpCAM 67.7% 63.5% M2PK IL13 TGFbeta1Mac2BP IGFBP2 66.7% 63.5% M2PK IL13 TGFbeta1 IGFBP2 EpCAM 70.8% 63.5%Dkk3 M2PK MIP1beta IL13 TGFbeta1 69.8% 62.5% Dkk3 M2PK IL8 IL13 EpCAM65.6% 62.5% Dkk3 M2PK IL8 TGFbeta1 IGFBP2 70.8% 62.5% Dkk3 M2PK IL8Mac2BP IGFBP2 69.8% 62.5% Dkk3 M2PK IL13 TGFbeta1 Mac2BP 68.8% 62.5%Dkk3 M2PK TGFbeta1 Mac2BP TIMP1 68.8% 62.5% Dkk3 M2PK TGFbeta1 TIMP1EpCAM 67.7% 62.5% M2PK IL8 IL13 Mac2BP TIMP1 64.6% 62.5% M2PK IL8 IL13IGFBP2 TIMP1 66.7% 62.5% M2PK IL13 IGFBP2 TIMP1 EpCAM 68.8% 62.5% Dkk3M2PK MIP1beta IL13 IGFBP2 63.5% 61.5% Dkk3 M2PK IL8 TGFbeta1 TIMP1 64.6%61.5%

TABLE 12 Combination of seven biomarkers in serum that equal or exceed50% sensitivity at 95% specificity. Test Cross Validated Sensitivity atSensitivity at BM1 BM2 BM3 BM4 BM5 BM6 BM7 95% Specificity 95%Specificity Dkk3 M2PK IL8 Mac2BP IGFBP2 TIMP1 EpCAM 78% 56% Dkk3 M2PKMIP1beta IL8 Mac2BP IGFBP2 EpCAM 76% 64% Dkk3 M2PK IL8 IL13 Mac2BPIGFBP2 TIMP1 73% 69% Dkk3 M2PK IL8 TGFbeta1 Mac2BP IGFBP2 TIMP1 72% 68%Dkk3 M2PK IL8 TGFbeta1 Mac2BP IGFBP2 EpCAM 71% 62% Dkk3 M2PK MIP1betaIL8 Mac2BP IGFBP2 TIMP1 70% 64% Dkk3 M2PK IL8 IL13 Mac2BP IGFBP2 EpCAM70% 67% Dkk3 M2PK IL8 IL13 TGFbeta1 Mac2BP IGFBP2 69% 66% M2PK MIP1betaIL8 Mac2BP IGFBP2 TIMP1 EpCAM 69% 55% Dkk3 M2PK MIP1beta IL8 IL13 Mac2BPIGFBP2 67% 68% Dkk3 M2PK MIP1beta IL8 TGFbeta1 Mac2BP IGFBP2 67% 64%Dkk3 M2PK MIP1beta TGFbeta1 Mac2BP IGFBP2 TIMP1 65% 59% Dkk3 M2PK IL8IL13 TGFbeta1 IGFBP2 TIMP1 65% 52% Dkk3 M2PK IL8 TGFbeta1 IGFBP2 TIMP1EpCAM 64% 56% Dkk3 M2PK IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 64% 57% M2PKMIP1beta IL8 IL13 IGFBP2 TIMP1 EpCAM 64% 50% Dkk3 M2PK MIP1beta IL8 IL13Mac2BP TIMP1 63% 55% Dkk3 M2PK MIP1beta IL8 TGFbeta1 IGFBP2 EpCAM 63%58% Dkk3 M2PK MIP1beta IL13 TGFbeta1 Mac2BP IGFBP2 63% 53% Dkk3 M2PK IL8IL13 TGFbeta1 Mac2BP TIMP1 63% 59% M2PK MIP1beta IL8 TGFbeta1 Mac2BPIGFBP2 EpCAM 63% 59% Dkk3 M2PK MIP1beta IL8 TGFbeta1 Mac2BP TIMP1 62%51% Dkk3 M2PK MIP1beta IL8 IGFBP2 TIMP1 EpCAM 62% 56% Dkk3 M2PK MIP1betaIL13 Mac2BP IGFBP2 TIMP1 62% 39% Dkk3 M2PK IL8 IL13 TGFbeta1 TIMP1 EpCAM62% 43% Dkk3 M2PK IL8 TGFbeta1 Mac2BP TIMP1 EpCAM 62% 50% Dkk3 MIP1betaIL8 IL13 Mac2BP IGFBP2 TIMP1 62% 55% Dkk3 MIP1beta IL8 Mac2BP IGFBP2TIMP1 EpCAM 62% 47% M2PK MIP1beta IL8 IL13 Mac2BP IGFBP2 TIMP1 62% 56%M2PK IL8 TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 62% 49% Dkk3 M2PK IL8 IL13TGFbeta1 IGFBP2 EpCAM 60% 56% Dkk3 M2PK IL13 TGFbeta1 Mac2BP IGFBP2EpCAM 60% 55% M2PK MIP1beta IL8 IL13 Mac2BP IGFBP2 EpCAM 60% 53%

TABLE 13 Seven biomarker combinations with Sensitivity between 60% and52%. Test Cross Validated Sensitivity at Sensitivity at BM1 BM2 BM3 BM4BM5 BM6 BM7 95% Specificity 95% Specificity Dkk3 M2PK MIP1beta IL8 IL13TGFbeta1 EpCAM 59% 54% Dkk3 M2PK MIP1beta IL8 IL13 IGFBP2 EpCAM 59% 52%Dkk3 M2PK MIP1beta IL8 TGFbeta1 IGFBP2 TIMP1 59% 52% Dkk3 M2PK TGFbeta1Mac2BP IGFBP2 TIMP1 EpCAM 59% 47% Dkk3 MIP1beta IL8 IL13 TGFbeta1 Mac2BPIGFBP2 59% 52% Dkk3 MIP1beta IL8 TGFbeta1 Mac2BP IGFBP2 TIMP1 59% 45%Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP 58% 41% Dkk3 M2PK MIP1betaIL8 TGFbeta1 TIMP1 EpCAM 58% 50% Dkk3 M2PK IL8 IL13 IGFBP2 TIMP1 EpCAM58% 55% Dkk3 MIP1beta IL8 IL13 Mac2BP IGFBP2 EpCAM 58% 50% Dkk3 IL8 IL13TGFbeta1 Mac2BP IGFBP2 TIMP1 58% 52% M2PK IL8 IL13 Mac2BP IGFBP2 TIMP1EpCAM 58% 52% Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1 IGFBP2 57% 49% Dkk3M2PK MIP1beta IL8 IL13 TIMP1 EpCAM 57% 48% Dkk3 M2PK MIP1beta IL13Mac2BP IGFBP2 EpCAM 57% 51% Dkk3 M2PK MIP1beta TGFbeta1 Mac2BP IGFBP2EpCAM 57% 46% Dkk3 M2PK MIP1beta TGFbeta1 IGFBP2 TIMP1 EpCAM 57% 49%Dkk3 M2PK IL13 Mac2BP IGFBP2 TIMP1 EpCAM 57% 49% Dkk3 MIP1beta IL8TGFbeta1 Mac2BP IGFBP2 EpCAM 57% 50% M2PK MIP1beta IL8 IL13 TGFbeta1TIMP1 EpCAM 57% 43% M2PK IL8 IL13 TGFbeta1 IGFBP2 TIMP1 EpCAM 57% 52%Dkk3 M2PK MIP1beta Mac2BP IGFBP2 TIMP1 EpCAM 56% 47% Dkk3 IL8 IL13Mac2BP IGFBP2 TIMP1 EpCAM 56% 48% MIP1beta IL8 IL13 TGFbeta1 Mac2BPIGFBP2 TIMP1 56% 54% Dkk3 M2PK MIP1beta IL8 TGFbeta1 Mac2BP EpCAM 55%35% Dkk3 M2PK MIP1beta IL13 IGFBP2 TIMP1 EpCAM 55% 44% Dkk3 M2PK IL8IL13 TGFbeta1 Mac2BP EpCAM 55% 35% Dkk3 IL8 IL13 TGFbeta1 Mac2BP IGFBP2EpCAM 55% 50% Dkk3 IL8 IL13 TGFbeta1 IGFBP2 TIMP1 EpCAM 55% 54% Dkk3M2PK MIP1beta IL8 IL13 Mac2BP EpCAM 54% 49% Dkk3 M2PK MIP1beta IL8Mac2BP TIMP1 EpCAM 54% 39% Dkk3 M2PK IL13 TGFbeta1 IGFBP2 TIMP1 EpCAM54% 42% Dkk3 IL8 TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 54% 51% M2PKMIP1beta IL8 IL13 TGFbeta1 IGFBP2 EpCAM 54% 37% M2PK MIP1beta IL13Mac2BP IGFBP2 TIMP1 EpCAM 54% 41% M2PK IL8 IL13 TGFbeta1 Mac2BP IGFBP2TIMP1 54% 43% MIP1beta IL8 IL13 TGFbeta1 Mac2BP IGFBP2 EpCAM 54% 42%Dkk3 M2PK MIP1beta IL8 IL13 IGFBP2 TIMP1 53% 52% Dkk3 M2PK IL8 IL13Mac2BP TIMP1 EpCAM 53% 42% M2PK MIP1beta IL8 TGFbeta1 IGFBP2 TIMP1 EpCAM53% 34% M2PK MIP1beta IL13 TGFbeta1 Mac2BP IGFBP2 EpCAM 53% 45% IL8 IL13TGFbeta1 Mac2BP IGFBP2 EpCAM 53% 51% MIP1beta IL8 IL13 Mac2BP IGFBP2TIMP1 EpCAM 53% 39% MIP1beta IL8 TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 53%51%

TABLE 14 Seven biomarker combinations with sensitivity <53%. Test CrossValidated Sensitivity at Sensitivity at BM1 BM2 BM3 BM4 BM5 BM6 BM7 95%Specificity 95% Specificity M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP EpCAM52% 42% Dkk3 M2PK MIP1beta IL13 TGFbeta1 IGFBP2 EpCAM 51% 41% Dkk3MIP1beta IL8 IL13 TGFbeta1 TIMP1 EpCAM 51% 35% M2PK MIP1beta IL8 IL13TGFbeta1 IGFBP2 TIMP1 51% 38% M2PK MIP1beta IL8 TGFbeta1 Mac2BP IGFBP2TIMP1 51% 30% M2PK IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 51% 37% Dkk3M2PK MIP1beta IL8 IL13 TGFbeta1 TIMP1 50% 48% Dkk3 MIP1beta IL8 IL13TGFbeta1 Mac2BP TIMP1 50% 41% Dkk3 MIP1beta IL8 IL13 TGFbeta1 IGFBP2TIMP1 50% 42% M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP IGFBP2 50% 49% M2PKMIP1beta IL8 IL13 TGFbeta1 Mac2BP TIMP1 50% 35% MIP1beta IL8 IL13TGFbeta1 IGFBP2 TIMP1 EpCAM 50% 39% Dkk3 M2PK MIP1beta IL13 TGFbeta1IGFBP2 TIMP1 49% 44% Dkk3 MIP1beta IL8 IL13 TGFbeta1 Mac2BP EpCAM 49%33% M2PK IL8 IL13 TGFbeta1 Mac2BP TIMP1 EpCAM 49% 43% Dkk3 M2PK MIP1betaIL13 Mac2BP TIMP1 EpCAM 48% 43% Dkk3 MIP1beta IL8 IL13 Mac2BP TIMP1EpCAM 48% 38% Dkk3 MIP1beta IL8 TGFbeta1 Mac2BP TIMP1 EpCAM 48% 40% Dkk3MIP1beta IL13 Mac2BP IGFBP2 TIMP1 EpCAM 48% 31% Dkk3 IL8 IL13 TGFbeta1Mac2BP TIMP1 EpCAM 48% 38% M2PK MIP1beta IL8 IL13 Mac2BP TIMP1 EpCAM 48%33% M2PK MIP1beta IL13 TGFbeta1 IGFBP2 TIMP1 EpCAM 48% 45% M2PK MIP1betaTGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 48% 37% M2PK MIP1beta IL13 TGFbeta1Mac2BP TIMP1 EpCAM 47% 41% IL8 IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM47% 40% Dkk3 MIP1beta IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 46% 42% M2PKMIP1beta IL8 TGFbeta1 Mac2BP TIMP1 EpCAM 46% 30% Dkk3 M2PK MIP1betaTGFbeta1 Mac2BP TIMP1 EpCAM 45% 37% Dkk3 M2PK IL13 TGFbeta1 Mac2BP TIMP1EpCAM 45% 41% Dkk3 MIP1beta IL8 IL13 TGFbeta1 IGFBP2 EpCAM 45% 33% Dkk3MIP1beta IL8 IL13 IGFBP2 TIMP1 EpCAM 44% 40% Dkk3 MIP1beta IL8 TGFbeta1IGFBP2 TIMP1 EpCAM 44% 43% Dkk3 MIP1beta IL13 TGFbeta1 Mac2BP TIMP1EpCAM 44% 43% MIP1beta IL8 IL13 TGFbeta1 Mac2BP TIMP1 EpCAM 44% 28% Dkk3M2PK MIP1beta IL13 TGFbeta1 Mac2BP TIMP1 43% 31% Dkk3 MIP1beta IL13TGFbeta1 IGFBP2 TIMP1 EpCAM 42% 40% MIP1beta IL13 TGFbeta1 Mac2BP IGFBP2TIMP1 EpCAM 42% 31% Dkk3 M2PK MIP1beta IL13 TGFbeta1 Mac2BP EpCAM 41%23% Dkk3 M2PK MIP1beta IL13 TGFbeta1 TIMP1 EpCAM 41% 33% Dkk3 MIP1betaIL13 TGFbeta1 Mac2BP IGFBP2 EpCAM 41% 41% Dkk3 MIP1beta TGFbeta1 Mac2BPIGFBP2 TIMP1 EpCAM 41% 39% Dkk3 IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM41% 37% M2PK MIP1beta IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 32% 27%

TABLE 15 Sensitivity of nine biomarker combinations in plasma and serumsamples at 95% specificity. BM1 BM2 BM3 BM4 BM5 BM6 BM7 BM8 BM9 PlasmaSerum Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 73% 77%Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP IGFBP2 EpCAM 73% 77% Dkk3M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP TIMP1 EpCAM 54% 72% Dkk3 M2PKMIP1beta IL8 IL13 TGFbeta1 IGFBP2 TIMP1 EpCAM 58% 74% Dkk3 M2PK MIP1betaIL8 IL13 Mac2BP IGFBP2 TIMP1 EpCAM 74% 70% Dkk3 M2PK MIP1beta IL8TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 71% 78% Dkk3 M2PK MIP1beta IL13TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 57% 72% Dkk3 M2PK IL8 IL13 TGFbeta1Mac2BP IGFBP2 TIMP1 EpCAM 67% 76% Dkk3 MIP1beta IL8 IL13 TGFbeta1 Mac2BPIGFBP2 TIMP1 EpCAM 54% 55% M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP IGFBP2TIMP1 EpCAM 58% 69%

TABLE 16 Sensitivity of ten biomarker combinations in plasma and serumsamples at 95% specificity. BM1 BM2 BM3 BM4 BM5 BM6 BM7 BM8 BM9 BM1oPlasma Serum Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1EpCAM 70% 55% Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1EpCAM 73% 68%

It will be appreciated by persons skilled in the art that numerousvariations and/or modifications may be made to the invention as shown inthe specific embodiments without departing from the scope of theinvention as broadly described.

The present embodiments are, therefore, to be considered in all respectsas illustrative and not restrictive.

All publications discussed and/or referenced herein are incorporatedherein in their entirety.

The present application claims priority from AU 2010903140, the entirecontents of which are incorporated herein by reference.

Any discussion of documents, acts, materials, devices, articles or thelike which has been included in the present specification is solely forthe purpose of providing a context for the present invention. It is notto be taken as an admission that any or all of these matters form partof the prior art base or were common general knowledge in the fieldrelevant to the present invention as it existed before the priority dateof each claim of this application.

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1. A method for diagnosing or detecting colorectal cancer in a subject,the method comprising: i) determining the presence and/or level of atleast two biomarkers selected from the group consisting of IL-8, IGFBP2,MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1β, TGFβ1, and TIMP-1 in a samplefrom the subject, wherein the presence and/or level of the twobiomarkers is indicative of colorectal cancer.
 2. The method of claim 1,wherein the two biomarkers selected from M2PK, EpCam, IL-13, DKK-3, IL-8and IGFBP2.
 3. The method of claim 1, wherein the method comprisesdetermining the presence and/or level of expression of at least three ofthe biomarkers.
 4. The method of claim 3, wherein the three biomarkersare selected from the group consisting of M2PK, EpCam, IL-13, DKK-3,IL-8, IGFBP2, MIP1β, TGFβ1 and MAC2BP.
 5. The method of claim 4, whereinthe three biomarkers are as follows: i) DKK-3, M2PK, and IGFBP2; ii)M2PK, IGFBP2, and EpCAM; iii) M2PK, MIP1β, and TGFβ1; or iv) IL-8,IL-13, and MAC2BP.
 6. The method of claim 1, wherein the methodcomprises determining the presence and/or level of expression of atleast four of the biomarkers.
 7. The method of claim 6, wherein fourbiomarkers are as follows: i) DKK-3, M2PK, MAC2BP, and IGFBP2; ii) IL-8,IL-13, MAC2BP, and EpCam; iii) DKK3, M2PK, TGFβ1, and TIMP-1; iv) M2PK,MIP1β, IL-13, and TIMP-1; or v) IL-8, MAC2BP, IGFBP2, and EpCam.
 8. Themethod of claim 1, wherein the method comprises determining the presenceand/or level of at least five of the biomarkers.
 9. The method of claim1, wherein the method comprises determining the presence and/or level ofat least six of the biomarkers.
 10. The method of claim 1, wherein themethod comprises determining the presence and/or level of at least sevenof the biomarkers.
 11. The method of claim 1, wherein the methodcomprises determining the presence and/or level of at least eight of thebiomarkers.
 12. The method of claim 1, wherein the method comprisesdetermining the presence and or level of at least nine of thebiomarkers.
 13. The method of claim 1, wherein the method comprisesdetermining the presence and/or level of at least ten of the biomarkers.14. The method of claim 1, wherein the method comprises detecting thepresence and/or level of least one additional biomarker selected fromthe group consisting of ICF-I, IGF-II, IGF-BP2, Amphiregulin, VEGFA,VEGFD, MMP-1, MMP-2, MMP-3, MMP-7, MMP-9, TIMP-1, TIMP-2, ENA-78, MCP-1,MIP-1β, IFN-γ, IL-10, IL-13, IL-1β, IL-4, IL-8, IL-6, MAC2BP, Tumor M2pyruvate kinase, M65, OPN, DKK-3, EpCam, TGFβ-1, and VEGFpan.
 15. Themethod of claim 1, wherein the method diagnoses or detects colorectalcancer with a sensitivity of at least 50%.
 16. (canceled)
 17. (canceled)18. The method of claim 1, wherein the method diagnoses or detects DukesStage A colorectal cancer with a sensitivity of at least 50% and aspecificity of at least 95%; a sensitivity of at least 60% and aspecificity of at least 80%; or a sensitivity of at least 50% and aspecificity of at least 90%.
 19. (canceled)
 20. (canceled) 21.(canceled)
 22. (canceled)
 23. (canceled)
 24. (canceled)
 25. (canceled)26. The method of claim 1, wherein the method comprises: i) determiningthe presence and/or level of the biomarkers in the sample from thesubject; and ii) comparing the presence and/or level of the biomarkersrelative to a control, wherein a presence and/or level in the samplethat is different to the control is indicative of colorectal cancer. 27.The method of claim 1, wherein the sample comprises blood, plasma,serum, urine, platelets, magakaryocytes or faeces.
 28. A method oftreatment comprising: (i) diagnosing or detecting colorectal canceraccording to the method of claim 1; and (ii) administering a therapeuticsuitable for the treatment of colorectal cancer.
 29. A method formonitoring the efficacy of treatment of colorectal cancer in a subject,the method comprising treating the subject for colorectal cancer andthen detecting the presence and/or level of at least two biomarkersselected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1β,TGFβ1, and TIMP-1 in a sample from the subject, wherein an absence ofand/or reduction in the level of expression of the biomarkers aftertreatment as compared with before treatment is indicative of aneffective treatment.
 30. (canceled)
 31. (canceled)